Data-driven haptic perception for robot-assisted dressing

Dressing is an important activity of daily living (ADL) with which many people require assistance due to impairments. Robots have the potential to provide dressing assistance, but physical interactions between clothing and the human body can be complex and difficult to visually observe. We provide evidence that data-driven haptic perception can be used to infer relationships between clothing and the human body during robot-assisted dressing. We conducted a carefully controlled experiment with 12 human participants during which a robot pulled a hospital gown along the length of each person's forearm 30 times. This representative task resulted in one of the following three outcomes: the hand missed the opening to the sleeve; the hand or forearm became caught on the sleeve; or the full forearm successfully entered the sleeve. We found that hidden Markov models (HMMs) using only forces measured at the robot's end effector classified these outcomes with high accuracy. The HMMs' performance generalized well to participants (98.61% accuracy) and velocities (98.61% accuracy) outside of the training data. They also performed well when we limited the force applied by the robot (95.8% accuracy with a 2N threshold), and could predict the outcome early in the process. Despite the lightweight hospital gown, HMMs that used forces in the direction of gravity substantially outperformed those that did not. The best performing HMMs used forces in the direction of motion and the direction of gravity.

[1]  Takamitsu Matsubara,et al.  Reinforcement learning of clothing assistance with a dual-arm robot , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  Kimitoshi Yamazaki,et al.  Bottom dressing by a life-sized humanoid robot provided failure detection and recovery functions , 2014, 2014 IEEE/SICE International Symposium on System Integration.

[4]  James M. Rehg,et al.  Haptic classification and recognition of objects using a tactile sensing forearm , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  J. Wiener,et al.  Measuring the activities of daily living: comparisons across national surveys. , 1990, Journal of gerontology.

[6]  Chih-Hung King,et al.  Informing assistive robots with models of contact forces from able-bodied face wiping and shaving , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[7]  Takamitsu Matsubara,et al.  Estimation of Human Cloth Topological Relationship using Depth Sensor for Robotic Clothing Assistance , 2013, AIR '13.

[8]  Wendy A. Rogers,et al.  Identifying the Potential for Robotics to Assist Older Adults in Different Living Environments , 2014, Int. J. Soc. Robotics.

[9]  Stefan Schaal,et al.  Skill learning and task outcome prediction for manipulation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Alexander Medvedev,et al.  An on-line algorithm for anomaly detection in trajectory data , 2012, 2012 American Control Conference (ACC).

[11]  Blake Hannaford,et al.  Haptic characteristics of some activities of daily living , 2010, 2010 IEEE Haptics Symposium.

[12]  Charles C. Kemp,et al.  Multimodal execution monitoring for anomaly detection during robot manipulation , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Trevor Darrell,et al.  Using robotic exploratory procedures to learn the meaning of haptic adjectives , 2013, 2013 IEEE International Conference on Robotics and Automation.

[14]  Nishanth Koganti,et al.  Cloth dynamics modeling in latent spaces and its application to robotic clothing assistance , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  Heather Janiszewski Goodin,et al.  The nursing shortage in the United States of America: an integrative review of the literature. , 2003, Journal of advanced nursing.

[16]  James M. Rehg,et al.  Rapid categorization of object properties from incidental contact with a tactile sensing robot arm , 2013, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids).

[17]  Advait Jain,et al.  Improving robot manipulation with data-driven object-centric models of everyday forces , 2013, Auton. Robots.

[18]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..

[19]  Manuela M. Veloso,et al.  Personalized Assistance for Dressing Users , 2015, ICSR.

[20]  Alberto Rodriguez,et al.  Failure detection in assembly: Force signature analysis , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[21]  Yiannis Demiris,et al.  User modelling for personalised dressing assistance by humanoid robots , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).